

import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


lowercase			:				Optional[int]   =     "sshleifer/mar_enro_6_3_student"







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Any:
														super().setUp()

														A      :					Optional[Any]      		=					cached_path(
														    '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''    ,						extract_compressed_file=__UpperCAmelCase    ,						)
														A      :					List[Any]      		=					f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'




							@slow
							@require_torch_gpu
							def       snake_case			(					self						)		->       Optional[int]:
														MarianMTModel.from_pretrained(__UpperCAmelCase						)






							@slow
							@require_torch_gpu
							def       snake_case			(					self						)		->       List[Any]:
														A      :					int      		=					{
														    '''$MAX_LEN''': 64,
														    '''$BS''': 64,
														    '''$GAS''': 1,
														    '''$ENRO_DIR''': self.data_dir,
														    '''facebook/mbart-large-cc25''': MARIAN_MODEL,
														    # "val_check_interval=0.25": "val_check_interval=1.0",
														    '''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
														    '''--num_train_epochs 6''': '''--num_train_epochs 1''',
														}

														# Clean up bash script
														A      :					str      		=					(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''						)[1].strip()
														A      :					List[str]      		=					bash_script.replace('''\\\n'''    ,						''''''						).strip().replace('''"$@"'''    ,						''''''						)
														for k, v in env_vars_to_replace.items():
																					A      :					Optional[int]      		=					bash_script.replace(__UpperCAmelCase    ,						str(__UpperCAmelCase						)						)
														A      :					int      		=					self.get_auto_remove_tmp_dir()

														# bash_script = bash_script.replace("--fp16 ", "")
														A      :					str      		=					f'\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        '.split()
														# XXX: args.gpus > 1 : handle multi_gpu in the future

														A      :					Any      		=					['''finetune.py'''] + bash_script.split() + args
														with patch.object(__UpperCAmelCase    ,						'''argv'''    ,						__UpperCAmelCase						):
																					A      :					Dict      		=					argparse.ArgumentParser()
																					A      :					List[str]      		=					pl.Trainer.add_argparse_args(__UpperCAmelCase						)
																					A      :					int      		=					SummarizationModule.add_model_specific_args(__UpperCAmelCase    ,						os.getcwd()						)
																					A      :					Optional[Any]      		=					parser.parse_args()
																					A      :					List[Any]      		=					main(__UpperCAmelCase						)

														# Check metrics
														A      :					Tuple      		=					load_json(model.metrics_save_path						)
														A      :					Union[str, Any]      		=					metrics['''val'''][0]
														A      :					Any      		=					metrics['''val'''][-1]
														self.assertEqual(len(metrics['''val''']						)    ,						(args.max_epochs / args.val_check_interval)						)
														assert isinstance(last_step_stats[f'val_avg_{model.val_metric}']    ,						__UpperCAmelCase						)

														self.assertGreater(last_step_stats['''val_avg_gen_time''']    ,						0.0_1						)
														# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
														self.assertLessEqual(last_step_stats['''val_avg_gen_time''']    ,						1.0						)

														# test learning requirements:

														# 1. BLEU improves over the course of training by more than 2 pts
														self.assertGreater(last_step_stats['''val_avg_bleu'''] - first_step_stats['''val_avg_bleu''']    ,						2						)

														# 2. BLEU finishes above 17
														self.assertGreater(last_step_stats['''val_avg_bleu''']    ,						17						)

														# 3. test BLEU and val BLEU within ~1.1 pt.
														self.assertLess(abs(metrics['''val'''][-1]['''val_avg_bleu'''] - metrics['''test'''][-1]['''test_avg_bleu''']						)    ,						1.1						)

														# check lightning ckpt can be loaded and has a reasonable statedict
														A      :					Optional[int]      		=					os.listdir(__UpperCAmelCase						)
														A      :					List[str]      		=					[x for x in contents if x.endswith('''.ckpt'''						)][0]
														A      :					List[str]      		=					os.path.join(args.output_dir    ,						__UpperCAmelCase						)
														A      :					Dict      		=					torch.load(__UpperCAmelCase    ,						map_location='''cpu'''						)
														A      :					List[str]      		=					'''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
														assert expected_key in ckpt["state_dict"]
														assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

														# TODO: turn on args.do_predict when PL bug fixed.
														if args.do_predict:
																					A      :					Tuple      		=					{os.path.basename(__UpperCAmelCase						) for p in contents}
																					assert "test_generations.txt" in contents
																					assert "test_results.txt" in contents
																					# assert len(metrics["val"]) ==  desired_n_evals
																					assert len(metrics['''test''']						) == 1







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							@timeout_decorator.timeout(6_00						)
							@slow
							@require_torch_gpu
							def       snake_case			(					self						)		->       int:
														A      :					Dict      		=					f'{self.test_file_dir_str}/test_data/wmt_en_ro'
														A      :					Optional[Any]      		=					{
														    '''--fp16_opt_level=O1''': '''''',
														    '''$MAX_LEN''': 1_28,
														    '''$BS''': 16,
														    '''$GAS''': 1,
														    '''$ENRO_DIR''': data_dir,
														    '''$m''': '''sshleifer/student_marian_en_ro_6_1''',
														    '''val_check_interval=0.25''': '''val_check_interval=1.0''',
														}

														# Clean up bash script
														A      :					Optional[int]      		=					(
														    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''						)[1].strip()
														)
														A      :					Dict      		=					bash_script.replace('''\\\n'''    ,						''''''						).strip().replace('''"$@"'''    ,						''''''						)
														A      :					Optional[int]      		=					bash_script.replace('''--fp16 '''    ,						''' '''						)

														for k, v in env_vars_to_replace.items():
																					A      :					List[Any]      		=					bash_script.replace(__UpperCAmelCase    ,						str(__UpperCAmelCase						)						)
														A      :					Dict      		=					self.get_auto_remove_tmp_dir()
														A      :					str      		=					bash_script.replace('''--fp16'''    ,						''''''						)
														A      :					List[str]      		=					6
														A      :					Tuple      		=					(
														    ['''distillation.py''']
														    + bash_script.split()
														    + [
														        f'--output_dir={output_dir}',
														        '''--gpus=1''',
														        '''--learning_rate=1e-3''',
														        f'--num_train_epochs={epochs}',
														        '''--warmup_steps=10''',
														        '''--val_check_interval=1.0''',
														        '''--do_predict''',
														    ]
														)
														with patch.object(__UpperCAmelCase    ,						'''argv'''    ,						__UpperCAmelCase						):
																					A      :					Optional[int]      		=					argparse.ArgumentParser()
																					A      :					List[Any]      		=					pl.Trainer.add_argparse_args(__UpperCAmelCase						)
																					A      :					Dict      		=					SummarizationDistiller.add_model_specific_args(__UpperCAmelCase    ,						os.getcwd()						)
																					A      :					Dict      		=					parser.parse_args()
																					# assert args.gpus == gpus THIS BREAKS for multi_gpu

																					A      :					List[str]      		=					distill_main(__UpperCAmelCase						)

														# Check metrics
														A      :					List[Any]      		=					load_json(model.metrics_save_path						)
														A      :					Any      		=					metrics['''val'''][0]
														A      :					List[Any]      		=					metrics['''val'''][-1]
														assert len(metrics['''val''']						) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

														assert last_step_stats["val_avg_gen_time"] >= 0.0_1

														assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
														assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
														assert isinstance(last_step_stats[f'val_avg_{model.val_metric}']    ,						__UpperCAmelCase						)

														# check lightning ckpt can be loaded and has a reasonable statedict
														A      :					Tuple      		=					os.listdir(__UpperCAmelCase						)
														A      :					Optional[Any]      		=					[x for x in contents if x.endswith('''.ckpt'''						)][0]
														A      :					str      		=					os.path.join(args.output_dir    ,						__UpperCAmelCase						)
														A      :					List[Any]      		=					torch.load(__UpperCAmelCase    ,						map_location='''cpu'''						)
														A      :					Any      		=					'''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
														assert expected_key in ckpt["state_dict"]
														assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

														# TODO: turn on args.do_predict when PL bug fixed.
														if args.do_predict:
																					A      :					List[Any]      		=					{os.path.basename(__UpperCAmelCase						) for p in contents}
																					assert "test_generations.txt" in contents
																					assert "test_results.txt" in contents
																					# assert len(metrics["val"]) ==  desired_n_evals
																					assert len(metrics['''test''']						) == 1









import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
		import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class 					__lowercase  (						unittest.TestCase		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					load_tool('''text-to-speech'''						)
														self.tool.setup()




							def       snake_case			(					self						)		->       Union[str, Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					Optional[Any]      		=					self.tool('''hey'''						)
														A      :					List[str]      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






							def       snake_case			(					self						)		->       Optional[Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					int      		=					self.tool('''hey'''						)
														A      :					Any      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






